Integrating Insights

Digital pathology sits at the heart of AI in healthcare. Bridging the gap between digital imaging data and its clinical interpretation - or ‘explainability’ - ensures AI tools remain accountable and trusted in medical practice. 

Enhancing Outcomes

  • Convolutional Neural Network; CNN; Gleason Grade; Heat map; prostate cancer; prostate cancer staging;

    Driving these diagnostics are image analysis (IA) algorithms derived from deep learning (DL) networks.

    Sophisticated algorithms are crafted from a comprehensive array of WSIs and associated metadata - sourced from the laboratory.

  • heatmap

    A shared language bridges medical expertise and technological innovation.

    Development must involve collaborative inputs from both pathologists and engineers.

  • Embracing multiple domains

    Computer vision, operations expertise, and data science are just some. Beyond traditional training, interdisciplinary collaboration is required to effectively manage and interpret vast datasets, develop AI, and ensure the responsible implementation of technology in clinical practice.

  • heat map, WSI, whole slide image

    Multimodal data is infused in technology-enabled clinical and research workflows throughout the globe.

    Pathologists are adopting new roles as bioinformaticians and computer visionaries of advanced medical solutions, bridging the clinical-computational divide to advance patient care.

Pathologists are stewards of laboratory data, establishing diagnostic ground truth for AI model validation. With 70% of medical decisions driven by laboratory diagnostics, pathologists' expertise forms the foundation of explainable AI. While current AI solutions focus on imaging, the future lies in integrating broader laboratory data.